Aim:A discrimination analysis has been explored for the probabilistic classifica-tion of healthy versus ovarian cancer serum samples using proteomics data from mass spectrometry (MS).Methods:The method employs data normalization,clustering,and a linear discriminant analysis on surface-enhanced laser desorp-tion ionization (SELDI) time-of-flight MS data.The probabilistic classification method computes the optimal linear discriminant using the complex human blood serum SELDI spectra.Cross-validation and training/testing data-split experi-ments are conducted to verify the optimal discriminant and demonstrate the accu-racy and robustness of the method.Results:The cluster discrimination method achieves excellent performance.The sensitivity,specificity,and positive predic-tive values are above 97% on ovarian cancer.The protein fraction peaks,which significantly contribute to the classification,can be available from the analysis process.Conclusion:The discrimination analysis helps the molecular identities of differentially expressed proteins and peptides between the healthy and ovarian patients.